Recognition of handwritten digits using proximal support vector machine

Handwritten Digit Recognition System involves reception and interpretation of handwritten digits by a machine. Due to variation in shape and orientation of handwritten digits, it is difficult for a machine to interpret handwritten digits. Handwritten digit Recognition has a wide area of research due to its vast applications like automatic bank cheques processing, billing and automatic postal service. In this thesis, an Offline Handwritten Digit Recognition System is presented. The recognition system is broadly divided into 2 parts, first part is feature extraction from handwritten images and the second one is classification of feature vector into digits. We propose descriptors for handwritten digit recognition based on Histogram of Oriented Gradient (HOG) feature .It is one of the widely used feature vector for object detection in computer vision. For classification of features, linear Proximal Support Vector Machine (PSVM) Classifier is proposed. This is a binary class classifier which is further converted to a 10 class classifier by means of One against all algorithm. Due to small training time, PSVM classifier is preferable over standard Support Vector Machine (SVM) Classifier. The handwritten images both for training and testing are taken from MNIST database. The performance of the system is measured in terms of Sensitivity, Accuracy, Positive Predictivity and Specificity. The performance of PSVM classifier is better compared to Artificial Neural Network(ANN)

Analysis Of Failure In Offline English Alphabet Recognition With Data Mining Approach

Offline handwriting recognition is a long existing approach to identify the handwritten phrase, letters or digits. Earlier studies in the handwriting recognition field were mostly focused on recognizing characters using Neural Network Language Model (NNLM) classifier, Hidden Markov Model (HMM), and Support Vector Machine (SVM) with segmentation technique, Hough Transform method, and structural features. However, these approaches involve complex algorithms and require voluminous dataset as the training model. Therefore, this study attempts a data mining approach to the analysis of failure in offline English alphabet recognition. The objectives of the study are to improve the pattern recognition approach for classifying English alphabets and to determine the root of classification failure in handwritten English alphabets. Handwritten data of capital letters of the English alphabet by 50 Universiti Sains Malaysia student experimented. The data was pre-processed to remove the outliers prior to classification analysis with the aid of the Waikato Environment for Knowledge Analysis (WEKA) tool. Classification analysis was initially performed on all seven classifier’s algorithms at 10-fold dross validation mode. At phase one, Stroke and Curve are added into the dataset and classified respectively. At phase two, Sharp Vertex, Closed Region, and Points are added in the dataset. The top three classification algorithms were selected: IBk, LMT and Random Committee for further classification. The classified result was further analyzed to identify the root of classification errors. At the raw dataset classification, the classification accuracy is low with 25%. As the attributes are added to raw dataset respectively, the accuracy of classification was successfully increased to 89%. Conclusively, the accuracy of the classification depends on the added attributes to distinguish characteristics of the alphabets

Incremental learning algorithms and applications

International audienceIncremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years

A novel prototype generation technique for handwriting digit recognition

The aim of this paper is to introduce a novel prototype generation technique for handwriting digit recognition. Prototype generation is approached as a two-stage process. The first stage uses an Adaptive Resonance Theory 1 (ART1) based algorithm to select an effective initial solution, while the second one executes a fine tuning designed to generate the best prototypes. To this end, the second stage deals with an optimization problem, in which the objective function to be minimized is the cost function associated to the classification. A naive evolution strategy is used to generate the prototype set able to reduce classification time, without greatly affecting the accuracy. Moreover, as the ART1 based algorithm has incremental learning capability, the first stage is also useful for selecting the prototype set according to variations in handwriting style. The classification task is performed by the k-nearest neighbor classifier. Experimental tests on the MNIST dataset demonstrated that our technique represents a good trade-off among accuracy, classification speed and robustness to handwriting style changes